mindquantum.algorithm.qaia.DSB

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class mindquantum.algorithm.qaia.DSB(J, h=None, x=None, n_iter=1000, batch_size=1, dt=1, xi=None, device='cpu', precision='float32')[source]

Discrete SB algorithm.

Reference: High-performance combinatorial optimization based on classical mechanics.

Parameters

  • J (Union[numpy.array, csr_matrix]) – The coupling matrix with shape \((N x N)\).

  • h (numpy.array) – The external field with shape \((N, )\).

  • x (numpy.array) – The initialized spin value with shape \((N x batch_size)\). Default: None.

  • n_iter (int) – The number of iterations. Default: 1000.

  • batch_size (int) – The number of sampling. Default: 1.

  • dt (float) – The step size. Default: 1.

  • xi (float) – positive constant with the dimension of frequency. Default: None.

  • device (str) – The device to use for computation ('cpu' or 'gpu'). Default: 'cpu'.

  • precision (str) – Precision type when using GPU ('float32', 'float16', 'int8'). Default: 'float32'.

update()[source]

Dynamical evolution based on Modified explicit symplectic Euler method.